Method and Apparatus Based on Combination of Physiological Parameters for Assessment of Analgesia During Anesthesia or Sedation

ABSTRACT

The invention relates to a method for monitoring a condition of a patient under anesthesia or sedation, whereupon one, two, three or more signals are acquired, and the signal(s) represent(s) cardiovascular and/or combined electrical biopotential on skull activity of the patient. From said signal or signals are derived or calculated at least two parameter values related to a quantity like waveform amplitude, waveform periodicity, waveform morphology, waveform variability, energy, power, signal complexity and frequency content. A predetermined mathematical index for probability of patient comfort is used, in which function said parameters are variables, and successively changing probability index values of said mathematical index is calculated.

FIELD OF THE INVENTION

The present invention relates to a method and an apparatus formonitoring a condition of a patient under anesthesia or sedation, while,in particular, information acquired from different sources in thepatient is utilized.

BACKGROUND OF THE INVENTION

Concept of the depth of anesthesia has been of interest for recentdecades, and several measures have been proposed to assess the depth ofanesthesia. Recently, however, this unitary anesthesia theory of theexistence of one-dimensional concept called “depth of anesthesia” hasbeen strongly criticized as oversimplified. Instead it has beensuggested that the anesthesia has not one but three main components:hypnosis, analgesia and muscle relaxation. Different anesthetic regimenshave different effect on these three components. Furthermore, they haveeffects on both cortical and sub-cortical levels. An adequate anesthesiameans unresponsiveness to both noxious and non-noxious stimuli. Theformer may be defined by means of hemodynamic, motor and endocrinestability, while the latter is related to the loss of consciousness andrecall and amnesia. In practice the adequate anesthesia is administeredby using a combination of drugs with different effects on brain, spinalcord, autonomic nervous system and neuromuscular junction. Thecombination of these effects hence creates the hypnotic, analgesic andmuscle relaxing effects.

In general anesthesia the patient is conducted through the phases ofanesthesia from the induction to the varying lengths of maintenanceperiod and to the final emergence out from anesthesia. Though thepatient does not usually recall any surgical events or perceive surgicalpain, the recovery and the post-operative comfort of the patient veryoften depend on the quality of the anesthesia during the operationitself. Adequate administration of analgesic drugs—meaning thatover-doses and under-doses can be avoided during anesthesia—is believedto advance the recovery of the patient. It has been suggested that thisis due to two main reasons. Surgical pain may sensitize the painpathways during surgery and thus lower the pain threshold in such a waythat even rather intense pain management in the post-operative period isineffective. It is said that the best way to avoid post-operative painis a good and adequate administration of analgesics during operation.The other mechanism is probably related to the secretions of stresshormones during surgery. These hormones may have their effects longafter surgery and can slow down the physical and psycho-physiologicalheeling of the patient. Adequate pain medications can suppress theautonomic nervous system and prevent excess secretion of these stresshormones. In this context the term “nociception” is commonly used torefer to the perception of pain. The receptors involved in paindetection are aptly enough referred to as nociceptors. Nociceptive inputis conveyed from the peripheral end organs to the central nervoussystem. Projection neurons in the spinal dorsal horn project to cellnuclei in supraspinal areas such as the thalamus, the brainstem, themidbrain etc. Of these, the synaptic junctions in the thalamus play avery important role in the integration and modulation of spinalnociceptive and non-nociceptive inputs. Nociceptive inputs are finallyconducted to the cortex, where the sensation of pain is perceived.Stimulation of these central nervous system regions either electricallyor chemically, e.g. by morphine and other opiates, produces analgesia inhumans. Currently the anesthesia practices rely on rather subjectiveassessments of the adequacy of the drug treatment during anesthesia.Anesthesiologists observe the patient and decide for the proper drugsthey give to the patient. Though this often is enough to avoid adverseevents such as arousal or muscle movements during surgery, which in factvery seldom occur in normal anesthesia nowadays, more objective measuresfor the anesthesia are needed. Recently the progress in the biopotentialsignal analysis has lead in reasonable quantitative estimation of thehypnotic level of the patient, and thereby the titration of theanesthetic agents can be guided by these new measurements.

For instance, the neurological activity of the brain is reflected inbiopotentials available on the surface of the brain and on the scalp.Thus, efforts to quantify the extent of anesthesia-induced hypnosis haveturned to a study of these biopotentials. The biopotential electricalsignals are usually obtained by a pair, or plurality of pairs, ofelectrodes placed on the patients scalp at locations designated by arecognized protocol and a set, or a plurality of sets or channels, ofelectrical signals are obtained from the electrodes. These signals areamplified and filtered. The recorded signals comprise anelectroencephalogram or EEG, which normally has no obvious repetitivepatterns, contrary to other biopotential signals, like electrocardiogram(ECG). Among the purposes of filtering is to remove electromyographic(EMG) signals from the EEG signal. EMG signals result from muscleactivity of the patient and will appear in electroencephalographicelectrodes applied to the forehead or scalp of the patient. They areusually considered artifacts with respect to the EEG signals. Since EMGsignals characteristically have most of their energy in a frequencyrange from 40 Hz to 300 Hz, which is different than that of the EEG,major portions of the EMG signals can be separated from the contaminatedEEG signal.

A macro characteristic of EEG signal patterns is the existence ofbroadly defined low frequency rhythms or waves occurring in certainfrequency bands. Four such bands are recognized: Alpha waves are foundduring periods of wakefulness and may disappear entirely during sleep.The higher frequency Beta waves are recorded during periods of intenseactivation of the central nervous system. The lower frequency Theta andDelta waves reflect drowsiness and periods of deep sleep.

For clinical use, it is desirable to simplify the results of EEG signalanalysis of the foregoing, and other types, into a workable parameterthat can be used by an anesthesiologist in a clinical setting whenattending the patient. Various such parameters for relating EEG signaldata to the hypnotic state of the patient are discussed in theliterature. Several use frequency domain power spectral analysis. Theseparameters include peak power frequency (PPF), median power frequency(MPF), and spectral edge frequency (SEF). A peak power frequency (PPF)parameter uses the frequency in a spectrum at which occurs the highestpower in the sampled data as an indication of the depth of anesthesia.The median power frequency (MPF) parameter, as its name implies, usesthe frequency that bisects the spectrum. In the same fashion, thespectral edge frequency uses the highest frequency in the EEG signal. Toimprove the consistency of an indicator of the hypnotic state or depthof anesthesia, several parameters are often employed in combination. Forexample, the spectral edge frequency (SEF) parameter may be combinedwith the time-domain burst suppression ratio (BSR) parameter to improvethe consistency and accuracy with which the depth of anesthesia can beindicated. Also more complex combinations of parameters, like bispectralindex (BIS) have been described.

There are a number of concepts and analytical techniques directed to thecomplex nature of random and unpredictable signals, like EEG. One suchconcept is entropy. Entropy, as a physical concept, describes the stateof disorder of a physical system. Applying the concept of entropy to thebrain, the premise is that when a person is awake, the mind is full ofactivity and hence the state of the brain is more nonlinear, complex,and noise like. Since EEG signals reflect the underlying state of brainactivity, this is reflected in relatively more “randomness” or“complexity” in the EEG signal data, or, conversely, in a low level of“order.” As a person falls asleep or is anesthetized, the brain functionbegins to lessen and becomes more orderly and regular. As the activitystate of the brain changes, this is reflected in the EEG signals by arelative lowering of the “randomness” or “complexity” of the EEG signaldata or conversely, increasing “order” in the signal data. When a personis awake, the EEG data signals will have higher entropy and when theperson is asleep the EEG signal data will have a lower entropy.

According to the International Publication WO-02/32305 both the EEG andEMG signal data are typically obtained from the same set of electrodesapplied, for example, to the forehead of the patient. The EEG signalcomponent dominates the lower frequencies (up to about 30 Hz) containedin the biopotentials existing in the electrodes and EMG signal componentdominates the higher frequencies (about 50 Hz and above). The presenceof EMG signal can provide a rapid indication of theconscious-unconscious state of the patient. Importantly, because of thehigher frequency of the EMG data signal, the sampling time can besignificantly shorter than that required for the lower frequency EEGsignal data. This allows the EMG data to be computed more frequently sothat the overall diagnostic indicator can quickly indicate changes inthe state of the patient. In one embodiment of the publication, the EEGsignal data and the EMG signal data are separately analyzed andthereafter combined into a diagnostic index or indicator. As notedabove, because of the celerity with which changes in the anestheticstate of the patient can be determined from the EMG data, the overallindex can quickly inform the anesthesiologist of changes in the state ofthe patient.

Another way is to observe a photoplethysmographic (=PPG) signal, whichis obtained by measuring the intensity of light transmitted through orreflected by the tissue. The dynamic part of the signal is caused byvariations in blood volume and perfusion of the tissue, affectingscattering and absorption of the incident light. The most usualapplication of the signal is the measurement of the oxygen saturation ofblood. The pulse waveform of the PPG signal is closely similar to thatof the intra-arterial blood pressure. The waveform is reflecting theinteraction between left ventricular output, i.e. cardiac output orstroke volume, and the capacitance of the vascular tree, also calledvascular resistance. Blood pressure is determined by the cardiac output,which is stroke volume multiplied by heart rate, and vascularresistance. However, in addition to these global circulatory parameters,the dynamic capacitance of the vasculature affects also the nonlinearrelationship of PPG signal and circulatory parameters. Especiallycomplex is the relationship between the PPG waveform shape within onepulse and the integrated pulse-to-pulse variables. The PPG signal isrelated to the changes in peripheral tissue blood volume and bloodabsorptivity. As it is the blood flow, that causes the blood volumechanges, the PPG signal is hence indirectly related to local blood flow.The flow, in turn, depends on the pressure gradient and local vasculardynamic resistance and capacitance. The PPG measuring as such has beenutilized for a long time. For instance U.S. Pat. No. 6,117,075 disclosesa method and device for monitoring the depth of anesthesia (=DOA) duringsurgery by analyzing patterns and characteristics of oscillatoryphenomena in measured pulse pressure and skin temperature signals. Themethod utilizes pulse pressure and skin temperature oscillatory patternsdescribe the nature of sympathetic vasomotor tone. The method monitorsDOA in two ways. Spectral characteristics of skin temperature or pulsepressure oscillatory phenomena are used to describe the depth ofanesthesia, and the concordance between oscillatory patterns of twophysiological signals, which have been recorded simultaneously but atdifferent locations, are used to describe the depth of anesthesia.According to the publication a PPG signal of an anesthetized patient iscontinuously monitored, and the recorded raw PPG signal is thenprocessed so as to generate a signal depicting the beat-to-beat pulsepressure amplitude. Then the signal is derived by detecting peaks, andcalculating the difference between each positive-negative peak pair,after which a further signal is processed in a manner so as to derive adata set describing very low frequency variations in pulse pressure overtime in the 0.01-0.04 Hz range, that is, the PPG signal amplitudevariability. Power spectrum analysis is finally performed on saidfurther signal, and the received frequency power spectrumcharacteristics are used to describe the DOA, such that a progressivelynarrower bandwidth describe a progressively deeper level of anesthesia.

However, it appears that the position of the dicrotic notch as well asthe PPG amplitude are dependent on various other sources than the statusof vasoconstriction or vasodilatation, including fluid balance,temperature of the site of PPG measurement, heart rate, etc. Hence,these parameters may be interpreted with caution. Furthermore, theyrefer to the usage of the PPG information as a measure of the depth ofanesthesia, which is an oversimplified one-dimensional assumption asdescribed above.

The publication E. Seitsonen, M. van Gils, I. Korhonen, K Korttila, A.Yii-Hankala: “EEG, Heart Rate, Pulse Plethysmography and MovementResponses to Skin Incision”—A-582, 2002 ASA Meeting Abstracts, Oct. 16,2002, discloses studies concerning evaluation of analgesia andnociception during general anesthesia. Raw EEG, the bispectral index,electrocardiography (ECG) and PPG data were collected and analyzedoffline. RR-interval (RRI) tachogram was derived from ECG and frontalelectromyography (FEMG) from EEG, and several beat-to-beat morphologyparameters were derived from PPG signal, as well as various time andfrequency domain parameters were computed from EEG, RRI and PPG data.When derived variables calculated as ratios or differences betweenpost-incision and pre-incision values were compared, RRI, amplitude ofthe dicrotic notch in PPG, EEG spectral entropy and FEMG power appearedas primary variables in the optimal linear discriminant function betweenmovers and non-movers. Finally it was concluded that combination ofthese parameters may be useful in assessing the level of analgesia andnociception during anesthesia. However, the publication does not provideany practical procedure for monitoring a patient.

U.S. Pat. No. 6,338,713, discloses a system and method for providinginformation to the user of a medical monitoring or diagnostic device toaid in the clinical decision making process. The preferred embodimentuses two estimators or predictors of the same physiological quantity,with each of the estimators being designed to detect specific states orartifacts in the estimated parameter and thus operating at a differentpoint on its respective ROC curve; one chosen to provide highsensitivity, the other chosen to provide high specificity. Thedivergence between the estimators is indicated by the use of a shadedregion between their respective time trends. The use of two estimatorsof the same parameters with different performance characteristics allowsthe system and method of the present invention to derive additionalinformation about the underlying physiologic process over and above thatwhich would be available from a single estimator. The system and methodof the present invention can derive information from not only theinstantaneous values of the estimators and the difference between them,but also from the time trend of the difference. Accordingly, thispublication is primarily directed to the accuracy of a measurement, butdoes not discuss the problem how the level of analgesia could bereliably monitored while the patient is under anesthesia or sedation.

Anyway, the administration of analgetics is still largely based on thevisual observations of the vital signs and the hemodynamic responses ofthe patient to surgical stimulation. Analgesic drugs are usually given,when the heart rate or blood pressure show fast increases or are in longterm at the high end of the normal ranges. Different motoric responses,sweating and lacrimation of the patient can be observed as well. Afurther problem not considered in the publications is the possiblesuppressive effect of relaxants on at least some signals acquired frompatient, which can have adverse effect on the reliability of the resultsreceived. The concept of analgesia is very complex and due to inter- andintraindividual variability the combined specificity and sensitivity forthe single-parameter based methods is not very good.

Accordingly the main object of the invention is to achieve a method andapparatus for monitoring the anesthesia or sedation of a patient so thata reliable data about level or depth of analgesia would be available toan anesthetist or to other purposes.

The second object of the invention is to achieve a method and apparatusfor monitoring the anesthesia or sedation capable of using measuredsignals derived from various sources of the patient, which means thatthe method should not be dependent on any single type of detector.

The third object of the invention is to achieve a method and apparatusfor monitoring the anesthesia or sedation capable to deliver suchresults as an output, with the basis of which the adequacy of analgesiacould be reliably enough assessed by inexperienced anesthetists or otheroperators, too.

The fourth object of the invention is to achieve a method and apparatusfor monitoring the anesthesia or sedation functioning with an acceptablespeed so that a change in analgesia to a hazardous direction is detectedand reported early enough to allow timely corrective actions.

SUMMARY OF THE INVENTION

According to the first aspect of the invention the method for monitoringa condition of a patient under anesthesia or sedation, comprises thesteps of: acquiring in real-time at least a first signal representing acardiovascular activity of the patient; deriving, from said firstsignal, continuously at least a first and a second instantaneousparameter value related to a quantity selected from a group ofquantities including waveform amplitudes, waveform periodicity, waveformmorphology, and waveform variability; applying a predeterminedmathematical index for probability of patient comfort, in which functionsaid at least first and second parameters are variables; calculatingsuccessively changing values of said mathematical index; and indicatingsaid successive index values.

In this case the first signal concerning cardiovascular activity ismeasured non-invasively using preferably photoplethysmography, though apressure metering can be also used, whereupon the quantity for the firstparameter value is a pulse wave amplitude, or a dicrotic notch height inthe pulse wave, and the quantity for the second parameter value is apulse rate, or a heart beat interval, or a temporal position of thedicrotic notch.

As an alternative, the method according to the invention comprises thesteps of: acquiring in real-time at least a first signal and a secondsignal representing a cardiovascular activity of the patient; deriving,from said first signal and second signal, continuously at least a firstand a third instantaneous parameter value related to a quantity selectedfrom a group of quantities including waveform amplitudes, waveformperiodicity, waveform morphology, and waveform variability; applying apredetermined mathematical index for probability of patient comfort, inwhich function said at least first and third parameters are variables;calculating successively changing values of said mathematical index; andindicating said successive index values.

In this case said first signal concerning cardiovascular activity isalso preferably measured non-invasively using photoplethysmography, ormeasured using a pressure metering, whereupon the quantity for saidfirst parameter value is a pulse wave amplitude, or a dicrotic notchheight in the pulse wave. The second signal concerning cardiovascularactivity is a cardiac excitation measured non-invasively usingelectrocardiogram, whereupon the quantity for said third parameter valueis a heart rate of the electrical excitation.

As a further alternative, the method according to the inventioncomprises the steps of: acquiring in real-time at least a first signaland a third signal representing a cardiovascular and respectively acombined electrical biopotential on skull activity of the patient;deriving, from said first signal, continuously at least a firstinstantaneous parameter value related to a quantity selected from agroup of quantities including waveform amplitude, waveform periodicity,waveform morphology, and waveform variability; calculating, from saidthird signal waveforms, continuously at least a fourth instantaneousparameter value related to a quantity selected from a group ofquantities including energy, power, signal complexity and frequencycontent, each over a predetermined time period; applying a predeterminedmathematical function for probability index of patient comfort, in whichfunction said at least first and fourth parameters are variables;calculating successively changing values of said mathematical index; andindicating said successive index values.

In this case the first signal concerning cardiovascular activity ismeasured non-invasively using photoplethysmography, just as above, andthe quantity for said first parameter value is a pulse wave amplitude,or a dicrotic notch height in the pulse wave. The third signal is anon-invasive measurement concerning neuromuscular and brain activitycomprising electromyography and electroencephalogram. The electricmyographic component is then extracted from said third signal, whichoriginally is a combination of EMG and EEG, as an EMG partial signal andelectro-encephalographic component as an EEG partial signal. Then thequantity for said fourth parameter value is a spectral power calculatedfrom said EMG partial signal, and the quantity for said fourth parametervalue is a subtraction of a response entropy, calculated from said thirdsignal as a whole, and a state entropy, calculated from said EEG partialsignal.

The first signal concerning cardiovascular activity can also be acardiac excitation measured non-invasively using electrocardiogram,whereupon the quantity for the first parameter value is a heart rate orinter beat interval of the electrical excitation.

In the context of this alternative it is also possible, and prefarableas is now believed, to acquiring online a second signal representing acardiovascular activity of the patient; deriving, from said and secondsignal, a third instantaneous parameter value related to a quantityselected from a group of quantities including waveform amplitudes,waveform periodicity, waveform morphology, and waveform variability; andintroducing said at least third parameter in said predeterminedmathematical function as an additional variable. Here the quantity forsaid first parameter value is a heart rate or inter beat interval of theelectrical excitation.

In all cases the first and the second parameter values, or the first andthe third parameter values, or the first and the fourth parameter valuesrespectively are normalized on the basis their respective parametervalues acquired continuously over a predetermined fixed time windowincluding or excluding the latest real-time parameter value. The abovementioned parameter values for normalizing are acquired either from saidpatient prior to incision, or prior to intubation, or prior to startinganesthesia or sedation, or from a group of patients prior to or duringincision and/or intubation and/or anesthesia or sedation. Themathematical index for probability is a nonlinear equation, or a neuralnetwork algorithm, or a defined or fuzzy rule-based reasoning procedure.

The successively calculated value or values is/are indicated or informedor made available to the operating person or persons for furtheractions, and/or possibly indicated or informed or made available to anadditional apparatus for further processing. It shall be noted that theabsolute value of the probability index according to the invention isthe most valid data made available to the personnel, though a change ofthe index may increase informativeness, too.

According to the second aspect of the invention the apparatus formonitoring a condition of a patient under anesthesia or sedation,comprises at least first sensor means for online receiving substantiallycontinuous electrical signal representing a cardiovascular activity ofthe patient; first time measuring means and a first voltage/currentdependent circuit connected with said sensor means; first memory meansstoring criteria of predetermined pulse wave parameters to be extractedfrom said signal; first deriving means connected to said first memorymeans, said first time measuring means and said first voltage/currentdependent circuit for continuously extracting first and second values ofsaid predefined pulse wave parameters; second calculation means forsuccessively performing a predetermined mathematical program havingtemporally variable probability index values, based on said first andsecond pulse wave parameter values, as an output; and a display and/orconnections into further devices.

As an alternative, the apparatus according to the invention comprises atleast first and second sensor means for online receiving substantiallycontinuous electrical signals representing a cardiovascular activity ofthe patient; first and second time measuring means and a first andsecond voltage/current dependent circuit connected with said sensormeans; first memory means storing criteria of predetermined pulse waveparameters to be extracted from said signals; first deriving meansconnected to said first memory means, said first time measuring meansand said first voltage/current dependent circuit for continuouslyextracting first values of said predefined pulse wave parameters; secondderiving means connected to said first memory means, said second timemeasuring means and said second voltage/current dependent circuit forcontinuously extracting third values of said predefined pulse waveparameters; second calculation means for successively performing apredetermined mathematical program having temporally variableprobability index values, based on said first and third pulse waveparameter values, as an output; and a display and/or connections intofurther devices.

As a further alternative, the method according to the inventioncomprises at least first and third sensor means for online receivingsubstantially continuous electrical signals representing acardiovascular activity and respectively an electrical biopotentialactivity of the patient; first and third time measuring means and afirst and third voltage/current dependent circuit connected with saidsensor means; first memory means storing criteria of predeterminedparameters to be extracted from said signals; first deriving meansconnected to said first memory means, said first time measuring meansand said first voltage/current dependent circuit for continuouslyextracting first values of predetermined pulse wave parameters; firstcalculation means connected to said first memory means, said third timemeasuring means and said third voltage/current dependent circuit forcontinuously extracting fourth values of predetermined biopotentialparameters; second calculation means for successively performing apredetermined mathematical program having temporally variableprobability index values, based on said first and fourth parametervalues, as an output; and a display and/or connections into furtherdevices.

Generally speaking, at least one height or amplitude parameter, i.e.parameter in y-direction in an orthogonal coordination, is derived orcalculated from the signal or signals for usage in the function ofprobability according to the invention, and at least one time ortemporal parameter, i.e. parameter in x-direction in an orthogonalcoordination, is also derived or calculated from the signal or signalsfor usage in the function of probability according to the invention. Inthis context the entropy parameter is considered to a height oramplitude parameter.

The main advantage of the method as compared to the state-of-art is thatby the method it is possible to significantly increase the specificityof analgesia monitoring as compared to a usage of a single variable asan input. The method is also practical in the sense that it is based onthe new usage of the signals which are either routinely monitored duringanesthesia, or which may be easily monitored with the existing patientmonitors, e.g PPG, EEG etc., and hence the method does not requireexpensive new sensors or monitoring means to be attached to the patient.

By combining information from various sources each related to level ofanalgesia but alone providing insufficient sensitivity and/orspecificity for analgesia assessment in practice, it is possible toimprove the quality of analgesia level monitoring. The most potentialinput signals comprise heart rate (as measured from ECG, pulseplethysmography, blood pressure or some other signal related tofunctioning of heart and providing beat-to-beat rhythm of the heart),plethysmographic pulse parameters (for example plethysmographic pulsenotch position information) and frontal electromyography signal (fEMG).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of a typical non-invasive PPG signal, ornon-invasive or invasive blood pressure metering signal, representingcardiovascular pulse wave or blood pressure of a patient, which signalcan be used as a starting raw signal in the invention.

FIG. 2 shows an example of a typical non-invasive ECG signalrepresenting cardiac excitation of a patient, which signal can be usedas a starting raw signal in the invention.

FIG. 3 shows an example of a typical non-invasive EEG+EMG signalrepresenting a combination of brain activity and neuromuscular activityof a patient, i.e. biopotentials, which signal can be used as a startingraw signal in the invention.

FIG. 4 shows a typical power spectrum of a biopotential signal EEG+EMG.

FIGS. 5A-5D are an example of real data recorded from a patientrepresenting the intermediate results and final probability indexcalculated according to the invention. FIG. 5D provides annotationsrelated to surgical procedures.

FIGS. 6-8 show the main steps of the preferred first, second and thirdembodiment, respectively, of the invention for processing the signalacquired from a patient.

FIGS. 9-11 show the main components of the preferred first, second andthird embodiment, respectively, of the invention as flow chart. Thecomponents shown can be separate electronic units, but quite as wellportions of a computer program or programs for microprocessors.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the first embodiment of the invention the combination of theparameter values used in the probability function P for the patientcomfort in anesthesia are obtained only from one cardiovascular signalS1. Though the outmost simplicity of this method in practice, multipleof quantities can be derived in the way that they still carry differenttype of information from the function of the autonomic nervous system(=ANS). The heart rate and its variability, which mainly reflects theparasympathetic activity, can be extracted from the periodicity of themeasured pulse wave, i.e. from the beat-to-beat pulse rate. On the otherhand the amplitude of the pulse wave, especially when measured inperipheral tissue, carries information from the skin circulation, whichis mainly controlled by the sympathetically branch of ANS. In case whenthe parasympathetical activity of the patient is stronger than thesympathetical activity, the heart rate is low, blood pressure is normaland peripheral circulation works normally as the blood vessels aredilated. If the patient has pain or is uncomfortable, the heart rate ishigher and blood vessels are more constricted, which results in smallerperipheral pulse wave amplitudes, especially in the plethysmographicwave. These signs of excess sympathetical activity result also inelevated blood pressures easily measured by continuous blood pressuremeters. In summary, the sympathetical activation, which increases heartrate, induces vasoconstriction in the peripheral circulation andincreases blood pressure, is an indication of patient discomfort.Usually this appears as a sudden change in the plethysmography waveformparameters and blood pressure. On the other hand the low activity of thesympathetical and parasympathetical branch of ANS and the balancebetween these regulation mechanisms is an indicator of adequate patientcomfort. Accordingly, it can be understood that even one signal cancarry quite a detailed information from the state of the autonomicnervous system and from the patient comfort.

In the simplest embodiment only oxygen saturation measurement with thepulse wave signal (PPG) is enough to calculate the probability index ofpatient comfort. As saturation measurement is in practice compulsory inanesthesia this advantageous embodiment of the invention is almostalways available to all patients.

In the second embodiment of the invention the combination of theparameter values used in the probability function P for the patientcomfort in anesthesia are obtained only from two cardiovascular signalsS 1 and S2. The heart rate is extracted from the ECG waveform instead ofthe pulse wave of the photoplethysmographic or blood pressuremeasurement. This is advantageous, because the heart beat interval canbe determined more accurately, and more detailed information can beextracted especially from the parasympathetical activity and from theparasympathetical—sympathetical balance of ANS. The better precision ofthe beat-to-beat interval is due to typically higher samplingfrequencies of the ECG measurement and more importantly due to the factthat the R-peak in the ECG waveform is very much sharper than the pulsewave peak. When the signal quality in PPG, due to poor bloodcirculation, is bad or when the PPG or BP waves are extremely round, theexact beat interval can still be determined with precision from the ECGwaveform. As the precision of the determination of the R-R interval fromECG—even without poor patient condition—is always very good, moreparameters, e.g. heart rate variability (HRV), can be extracted from thetemporal cardiac function. These improve the performance of theprobability index for the patient comfort and leads into bettersensitivity and specificity of the index.

In the third embodiment of the invention the combination of theparameter values used in the probability function P for the patientcomfort in anesthesia are obtained only from a cardiovascular signal S1and a biopontential signal S3 on skull or scalp. This is one of thepreferred embodiments, together with the embodiment utilizing threesignals S1, S2 and S3, regarding the specificity and sensitivity ofcombining parameters into one index of patient comfort, is achieved withthe biopotential measurement on patient skull. The cardiovascularmeasurement are good indicators, but sometimes too unspecific to patientcomfort as they are influenced by other psychological emotional triggersor by physiologic factors such as the intra-vascular volume status ofthe patient, by sepsis, arrhythmias, respiratory disturbance orhypertension. Therefore, in addition to the cardiovascular quantities,information is needed from the brain activity, its psychophysiologicalaspects and from the overall state of the central nervous system. Bybiopotential measurement on skull, including electroencephalography,electromyography and skin conductivity measurement, one can deriveinformation, which is specific to the cortical brain activity andhypnosis, to the facial muscle nerve activity and to the sudomotor(sweating) activity, respectively. Hypnosis describes the state of humancortex, especially various states of consciousness and unconsciousness,i.e. ability of cortical processing of the sensory nerve information.Facial muscle nerve activity describes the motoric control of thesemuscles (from cortex), but also unintentional facial expressions, whichoriginate from the brain stem level and reflect also the state of theautonomic nervous system or the state of the central nervous system atthe subcortical level. Finally the sudomotor activity is emotionallytriggered from the cortex, but as the facial ‘mimic’ muscles, itsactivation is often a result of painful or discomfortable stimulationwithout real perception of pain at the cortical level. It is well knownthat discomfort increases sweating—during anesthesia it is a commonpractice to sense the patient forehead for sweating, which amonganesthesiologist is often interpreted as a sign of discomfort even indeep hypnosis. It is believed that both the sudomotor andelectromyographic activity are specific indicators of the sympatheticalactivation and patient discomfort and that the cortical activity is anadditional parameter for the overall suppression in the central nervoussystem. All this information is complementary within itself, but incombination with the cardiovascular signals, it forms an entity, bywhich the patient discomfort and comfort can be estimated even in deephypnosis. Even more than that, the index of hypnosis and the index ofanalgesia can be made rather orthogonal to each other. This means thatthe indices are independent and that the anesthesiologist can makespecific decision about whether patient needs more analgesic or hypnoticdrugs.

If the biopotential measurement is arranged on the forehead of thepatient, both the facial muscle (FEMG) and brain (EEG) activitycomponents are present in the raw measured signal S3. In principle, thesame arrangement is suitable to measure the skin contact of theelectrodes with the scalp, in which the sweating of the forehead is aswell seen. We, however, omit this contribution and concentrate on thesignal with EEG and FEMG, only. We also limit our discussion to one ofthe advantageous embodiment of the invention, namely to a technique ofspectral signal entropy, the concept of which can be applied to both EEGand FEMG signal components.

Entropy, when considered as a physical concept, is proportional to thelogarithm of the number of microstates available to a thermodynamicsystem, and is thus related to the amount of “disorder” in the system.In this context, entropy describes the irregularity, complexity, orunpredictability characteristics of a signal. In a simple example, asignal in which sequential values are alternately of one fixed magnitudeand then of another fixed magnitude has an entropy value of zero, i.e.the signal is completely regular and totally predictable. A signal inwhich sequential values are generated by a random number generator hasgreater complexity and higher entropy. Entropy is an intuitive parameterin the sense that one can visually distinguish a regular signal from anirregular one. Entropy also has the property that it is independent ofabsolute scales such as the amplitude or the frequency of the signal: asimple sine wave is perfectly regular whether it is fast or slow. In anEEG application, this is a significant property, as it is well knownthat there are interindividual variations in the absolute frequencies ofthe EEG rhythms.

There are various ways to compute the entropy of a signal. In frequencydomain, spectral entropy may be computed in ways generally known. Thestarting point of the computations is the spectrum of the signal. Suchan algorithm is implemented in the Datex-Ohmeda Entropy® Module, inwhich the discrete Fourier transformation of the signal is calculated. Aflat spectrum indicates a high and peaky spectrum a low entropy value.Without going into more details, we next describe how the physicalconcept of spectral entropy is employed in clinical decision makingabout the patient comfort.

Referring to FIG. 4, it is informative to consider two entropyindicators, one over the EEG dominant frequency range alone and anotherover the complete range of frequencies, including both EEG and EMGcomponents. The State Entropy (SE) is computed over the frequency rangefrom 0.8 Hz to 32 Hz. It includes the EEG-dominant part of the spectrum,and therefore primarily reflects the cortical state of the patient. TheResponse Entropy (RE) is computed over a frequency range from 0.8 Hz to47 Hz. It includes both the EEG-dominant and EMG-dominant part of thespectrum. It is advantageous to normalize these two entropy parametersin such a way that RE becomes equal to SE when the EMG power, i.e. sumof spectral power between 32 Hz and 47 Hz, is equal to zero, as theRE-SE-difference then serves as an indicator for EMG activation.Consequently, RE varies from 0 to 1, whereas SE is always smallerthan 1. When there is EMG activity, spectral components within the range32-47 Hz differ significantly from zero and RE is larger than SE. Withthese definitions, SE and RE both serve their own informative purposesfor the anesthesiologists. The State Entropy is a quantity which isstable enough to provide the anesthesiologist at one glance of thenumber an idea of the current cortical state the patient is in. TheResponse Entropy, on the other hand, reacts fast to changes as it can becalculated using shorter time windows than SE.

There are several parameters that could be classified as describingwaveform morphology, including at least: the area under the pulse, FWHM(=full width at half maximum value of the pulse), amplitude/period,dicrotic notch height in absolute units, dicrotic notch height inrelative units (=height/amplitude), skewness of the pulse, anycharacteristics after derivation or multiple of derivations of thesignal, any parameter after FFT analysis (=first harmonic peak/basefrequency peak, etc), any signal processing or modelling parameter(=e.g. AutoRegressive-model of the signal). In this context theparameters that could be classified as describing waveform amplitudeinclude: the relative and absolute difference between the peak value andthe minimum value of the pulse wave. It is especially pointed out thathere the “wave” includes a bias, generally called as a dc-component ofthe signal, which variates considerably slower than the pulse, i.e. theac-component of the signal. Accordingly, there is pulsation of thesignal and a level of the signal, around which level said pulsationoccurs. There are some parameters that could be classified as describingwaveform periodicity, including at least: pulse rate, heart rate, heartbeat interval, temporal position of the dicrotic notch in absolute units(after the peak-position or after any fixed position in one singlepulse) or in relative units (=temporal position/period), P-R interval,Q-T interval, QRS duration. There are a plurality of parameters thatcould be classified as describing waveform variability (=changes of anywaveform characteristics with time), including at least: SD (=standarddeviation over a fixed time window), SSD (=SD of the successive signalpoints=SD after derivation of the signal), RMS-value (as above), LF(=low frequency variability of any waveform characteristics: is relatedwith the sympathetical activity of ANS), HF(=high frequency variabilityof any waveform characteristics: is related with the parasympatheticalactivity of ANS), Entropy of any characteristics (measure of disorder,see Spectral entropy of EEG). It is also noted here thatnon-stationarity of the signal is one cause for waveform variability andthe indices and methods, by which the non-stationarities are detected,produce variables, which shall be interpreted as waveform variability.Non-stationarity here is any statistically significant temporaldeviation of statistical measures of the signal from the average values.As to the signal complexity, there are at least the followingparameters: Spectral entropy, approximate entropy, Lempel-ZivComplexity, Golmogorov-Sinai entropy, Fractal exponents, bispectralindices, correlation lengths (in time). At least the followingparameters can be classified in frequency content: FFT spectrum derivedparameters like spectral entropy, power at different brain rhythm bands,median frequency, spectral edge frequency, 95% power edge frequency,mean frequency, peak amplitude frequency (=highest power frequency). Thepower and energy are self-explanatory parameters, and does not needfarther explanation.

In the FIGS. 1 to 3 the following parameters, which are the mostfrequently used and preferred parameters, are visualized: pulse waveamplitude y_(A); dicrotic notch height y_(N) in the pulse wave; heartbeat interval τ_(A), temporal position of the dicrotic notch τ_(N);systolic blood pressure Y_(S); and diastolic blood pressure Y_(D). Theentropies, which are frequently used and preferred parameters, too,cannot be visualized. The parameters listed above are all familiar to aperson skilled in the art so that it is not necessary to mark them inthe figures.

The normalizing discussed above means that the prevailing or present orlatest parameter value is subtracted from the calculated average valueof the historical parameter values, or vice versa, or the prevailing orpresent or latest parameter value is divided by the calculated averagevalue of the historical parameter values, or vice versa. One of these,two of these, or three of these, or all of these can be usedsimultaneously in the predetermined mathematical function according tothe invention. The average value can be a constant average value overtime period, or over a number of heart beat pulses, or a moving weightedor non-weighted average value over a time period or over a number ofheart beat pulses during operation of the patient, or a constant groupaverage over certain patient type(s).

The mathematical index for probability can be generally formed,according to the invention, several ways. Also the history data, i.e.the parameter values acquired continuously over a predetermined fixedtime window including or excluding the latest real-time or prevailingparameter value, can be processed several ways. The probability index ofthe invention combines in real-time the historical parameter values withthe prevailing parameter data from at least two sources. The index P orP(t) for probability of patient comfort is a function, which shall beunderstood to be either an equation, or an algorithm, or a reasoning orlogical procedure, in which at least two parameters are variables Thismathematical combination may be done by using some nonlinear equation,logical rules and/or operators, artificial neural networks, fuzzy logic,etc.

The index of patient comfort may be calculated by the followingnonlinear equation:

${P(t)} = \frac{1}{1 + ^{- {({{{aX}{(t)}} + {{bY}{(t)}} + {{cZ}{(t)}} + d})}}}$

where

P is the index for probability of patient comfort,

a, b, c, d are parameters selected to optimise the performance of theindex,

X(t), Y(t), Z(t) are monitored current values of the normalizedparameter values,

t is the sample time.

It should be noted that the coefficients of the mathematical equationmust be selected according to the quantities and parameters used andaccording to the available signal, and will depend on the exact choiceand implementation of the monitored ensamble of parameter history valuesand their normalization means.

The index of patient comfort may be also determined by using rule-basedreasoning. These rules are by translating clinical knowledge acquired inour experimental studies on nociception into formal “if . . . then . . .else” statements that can be directly implemented into a computerprogramming language. The probability index P is then calculated as asequence of reasoning statements. It is possible to use exactly definedrules as well as fuzzy rules in the reasoning. The value ranges of theparameters as well as the index for probability are in this casediscredited into certain intervals to allow for symbolic reasoning. Forexample the index P can be subdivided into three or more intervals; if0≦P≦0.3→P is labeled as having “low” value, if 0.3<P≦0.7→P is labeled ashaving “medium” value, and if 0.7<P≦1.0→P is labeled as having “high”value. Similar divisions could be done for the monitored values X(t),Y(t), Z(t). For example:

if ((X(t) = “high”) or (X(t) = “medium”)) then   P(t) := “low” else   if((Y(t) = “low”) and (Z(t) = “high”)) then     P(t) := “high”   else    P(t) := “medium”where

=comparison operator (test of equality),

:=assignment operator (the value of the operand on the right hand sideis assigned to the operand on the left hand side).

This example indicates that when X(t) has a medium or high value thenthe probability of nociception P is low. If X(t) has a low value andY(t) is low and Z(t) has a high value then P is high. In other cases Pis medium.

Further, the index of patient comfort may be also determined by usingpreviously recorded data that has been labeled at each time instancewith an indication of whether there was nociception or not, e.g.,annotated by an anesthetist. This kind of procedure can be called neuralnetwork. Typical examples cases of X(t), Y(t), and Z(t) as recordedduring instances of nociception and non-nociception can then beconstructed. This can be done by grouping vectors v(t) =(X(t), Y(t),Z(t)) that have been measured during nociception periods together into aset of i “typical” nociception examples, {vi} , and grouping vectors ofdata that have been measured during non-nociception periods into a setof j typical non-nociception examples, {wj}. This grouping can beimplemented with various different algorithms, e.g., averaging,self-organisation. Once we have constructed these sets we can use themto estimate a value of p(t) for data values recorded at t.

For data samples X(t), Y(t), Z(t) monitored at time t the vector u(t)=(X(t), Y(t),Z(t)) is constructed.

The distances D between u(t) and each element of {vi} and {wj} arecalculated. This gives i+j distance calculations (as distance measuree.g., the Euclidean distance can be used).

The smallest distance calculation result is noted.

If this smallest distance was obtained when comparing u(t) with anelement of {vi} then P(t) is assigned as being “high”, if it wasobtaining when comparing with an element of {wj} then P(t) is assignedas being “low”.

It is also possible to use more than those two parameters describedabove. Accordingly, it is possible add measurement of another typecardiovascular activity in the first embodiment of the invention so thatthere are two signals and three parameter values for calculation of theprobability index. In the second embodiment of the invention it ispossible to derive a further parameter from the two signals so thatthere are three parameter values for calculation of the probabilityindex. In the third embodiment of the invention it is possible to derivea further parameter from the two signals so that there are threeparameter values for calculation of the probability index, too. It shallbe understood that it is also possible to perform further onlinemeasurements, whereupon three signals are acquired, and three or four ormore parameters are included in the calculation of the probabilityindex.

EXAMPLE

In the following an example is described, which is based on anunpublished material concerning 23 patients, single-variable basedestimators based on a PPG-signal, and a fEMG-signal for the level ofanalgesia could classify the patients' responses to skin incision with65-74% correctness i.e. the physiological responses could discriminatethe patients who moved or did not move as a response to skin incision.When the information in these variables is combined the correctness fordetection of insufficient analgesia is improved to 83%. In the exampleof real-time data were recorded during abdominal hysterectomy surgery.After induction (FIG. 5D: Arrow A) with fentanyl 1 μg/kg iv and propofol1 mg/kg iv anesthesia was deepened with sevoflurane 8% in 100% oxygenvia facial mask until endotracheal intubation (FIG. SD: Arrow B).Sevoflurane concentration was adjusted to equal 0.8 MAC (1.6%end-tidal). Surgery began 14 min after intubation with skin incision(FIG. 5D: Arrow C), after which fentanyl and propofol were administered.

For the end purpose at least two signals were continuously acquired fromthe patients, in this example a pulseplethysmographic (PPG) signal and afacial electro-myographic (FEMG) signal. The instantaneous parametervalues of R-to-R interval (=RRIpost and RRIpre), dicrotic notch height(=PtyNotchAmppost and PtyNotch-Amppre), FEMG power (=fEMGpost andfEMGpre) were continuously derived or calculated the heart rate anddicrotic notch being the used quantities. A nonlinear combination of theabove parameters according to the invention was calculated, as well as alinear combination of the same parameters as a comparison. Therespective instantaneous parameter values were measured real-time oronline from each of the patients. The originally received raw signal,i.e. continuous curves of parameter values are not shown in the figures,but the curves normalized with a pre-incision reference level isvisualized. Accordingly, FIG. 5A discloses normalized R-to-R interval,FIG. 5B discloses normalized dicrotic notch height, FIG. 5C disclosesnormalized fEMG power, and FIG. 5D discloses index P for probability ofpatient comfort obtained by mathematical nonlinear combination of theabove mentioned normalized signals. In this mathematical function theending -post means the latest parameter value and the ending -pre meansthe calculated historical data of the respective parameter values, whichcan be also called a reference value. As can be seen there used bothsubraction and division between the latest value and the referencevalue. The time axis is in seconds.

The actual non-linear equation for the mathematical index forprobability was:

$P = \frac{1}{1 + ^{- {({{{- 8},624\frac{RRIpost}{RRIpre}} - {2.672{(\begin{matrix}{{PtyNotchAmppost} -} \\{PtyNotchAmppre}\end{matrix})}} + {0.412\frac{fEMGpost}{fEMGpre}} + 3.993})}}}$

where P is the probability of a movement as a response for noxiusstimulus. This index is an indicator for potential nociception duringanesthesia or sedation so that a high values of the index indicate highprobability for nociception and low values indicate low probability ofnociception.

Signs for nociception and inadequate analgesia are clearly seen aroundintubation (FIG. 5D: arrow B) and incision (FIG. 5D: arrow C) innonlinear combination parameter but not so clearly in individualvariables. Note also recovery from anesthesia (FIG. 5D: arrow D) whichis associated with an increase in the index.

1-22. (canceled)
 23. A method for monitoring a condition of a patientunder anesthesia or sedation, the method comprising the steps of:acquiring at least a first signal and a third signal representing acardiovascular and respectively a combined electrical biopotential onskull activity of the patient; —deriving, from said first signal, atleast a first parameter value; calculating, from said third signal, atleast a fourth parameter value; applying a predetermined mathematicalindex for probability of patient comfort, in which index said at leastfirst and fourth parameters are variables; calculating successivelychanging values of said mathematical index; and indicating saidsuccessive index values.
 24. A method according to claim 23, wherein atleast first parameter value is related to a quantity selected from agroup of quantities including waveform amplitude, waveform periodicity,waveform morphology, and waveform variability.
 25. A method according toclaim 23, wherein said at least fourth parameter value related to aquantity selected from a group of quantities including energy, power,signal complexity and frequency content, each over a predetermined timeperiod.
 26. A method according to claim 23, wherein said first signalconcerning cardiovascular activity is a blood volume signal measurednon-invasively using photoplethysmography.
 27. A method according toclaim 26, wherein: the quantity for said first parameter value is apulse wave amplitude, or a dicrotic notch height in the pulse wave; andthe quantity for said second parameter value is a pulse rate, or a heartbeat interval, or a temporal position of the dicrotic notch.
 28. Amethod according to claim 23, wherein said third signal is anon-invasive measurement concerning neuromuscular and brain activity.29. A method according to claim 28, wherein said neuromuscular and brainactivity measurement comprises electromyography andelectroencephalogram.
 30. A method according to claim 29, furthercomprising the step of extracting electric myographic component fromsaid third signal as an EMG partial signal, and electroencephalographiccomponent as an EEG partial signal.
 31. A method according to claim 30,wherein the quantity for said fourth parameter value is a spectral powercalculated from said EMG partial signal.
 32. A method according to claim29, wherein the quantity for said fourth parameter value is asubtraction of a response entropy, calculated from said third signal asa whole, and a state entropy, calculated from said EEG partial signal.33. A method according to claim 23, wherein said first signal concerningcardiovascular activity is a cardiac excitation measured non-invasivelyusing electrocardiogram.
 34. A method according to claim 33, wherein thequantity for said first parameter value is a heart rate or inter beatinterval of the electrical excitation.
 35. A method according to claim23, further comprising the steps of: acquiring online a second signalrepresenting a cardiovascular activity of the patient; deriving, fromsaid and second signal, a third parameter value related to a quantityselected from a group of quantities including waveform amplitudes,waveform periodicity, waveform morphology, and waveform variability; andintroducing said at least third parameter in said predeterminedmathematical index as an additional variable.
 36. A method according toclaim 35, wherein the quantity for said first parameter value is a heartrate or inter beat interval of the electrical excitation.
 37. A methodaccording to claim 23, wherein said mathematical index for probabilityis a nonlinear equation.
 38. A method according to claim 23, whereinsaid mathematical index for probability is a neural network algorithm.39. A method according to claim 23, wherein said mathematical index forprobability is based on a defined or fuzzy rule-based reasoningprocedure.
 40. A method according to claim 23, further comprising thestep of normalizing said first and fourth parameter values on the basistheir respective parameter values acquired over a predetermined fixedtime window including or excluding the latest real-time parameter value.41. A method according to claim 40, wherein said normalized parametervalues are acquired from: said patient prior to incision, or prior tointubation, or prior to starting anesthesia or sedation, or group ofpatients prior to or during incision and/or intubation and/or anesthesiaor sedation. 42-53. (canceled)